Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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Early prediction of intensive care unit admission in emergency department patients using machine learning.

BACKGROUND: The timely identification and transfer of critically ill patients from the emergency dep...

MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-Ray Self-Supervised Representation Learning.

Self-supervised learning (SSL) reduces the need for manual annotation in deep learning models for me...

Adaptive Annotation Correlation Based Multi-Annotation Learning for Calibrated Medical Image Segmentation.

Medical image segmentation is a fundamental task in many clinical applications, yet current automate...

Sleep Stage Classification Via Multi-View Based Self-Supervised Contrastive Learning of EEG.

Self-supervised learning (SSL) is a challenging task in sleep stage classification (SSC) that is cap...

Multi-View Multiattention Graph Learning With Stack Deep Matrix Factorization for circRNA-Drug Sensitivity Association Identification.

Identifying circular RNA (circRNA)-drug sensitivity association (CDsA) is crucial for advancing drug...

Asymmetric Multi-Task Learning for Interpretable Gaze-Driven Grasping Action Forecasting.

This work tackles the automatic prediction of grasping intention of humans observing their environme...

A machine-learning model for prediction of Acinetobacter baumannii hospital acquired infection.

BACKGROUND: Acinetobacter baumanni infection is a leading cause of morbidity and mortality in the In...

Deep learning-driven multi-omics sequential diagnosis with Hybrid-OmniSeq: Unraveling breast cancer complexity.

BackgroundBreast cancer results from an uncontrolled growth of breast tissue. Many methods of diagno...

Harnessing machine learning and multi-omics to explore tumor evolutionary characteristics and the role of AMOTL1 in prostate cancer.

Although recent advancements have shed light on the crucial role of coordinated evolution among cell...

Multi-Sensor Learning Enables Information Transfer Across Different Sensory Data and Augments Multi-Modality Imaging.

Multi-modality imaging is widely used in clinical practice and biomedical research to gain a compreh...

An omics-based tumor microenvironment approach and its prospects.

Multi-omics approaches are revolutionizing cancer research and treatment by integrating single-modal...

AI-assisted human clinical reasoning in the ICU: beyond "to err is human".

Diagnostic errors pose a significant public health challenge, affecting nearly 800,000 Americans ann...

PREDICTING IN-HOSPITAL MORTALITY IN CRITICAL ORTHOPEDIC TRAUMA PATIENTS WITH SEPSIS USING MACHINE LEARNING MODELS.

Purpose: This study aims to establish and validate machine learning-based models to predict death in...

Exploring an novel diagnostic gene of trastuzumab-induced cardiotoxicity based on bioinformatics and machine learning.

Trastuzumab (Tra)-induced cardiotoxicity (TIC) is a serious side effect of cancer chemotherapy, whic...

Liver tumor segmentation method combining multi-axis attention and conditional generative adversarial networks.

In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and t...

MNet: A multi-scale network for visible watermark removal.

Superimposing visible watermarks on images is an efficient way to indicate ownership and prevent pot...

Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation.

BACKGROUND: Suicide is a leading cause of death worldwide, making early identification of suicidal b...

Accurate Airway Tree Segmentation in CT Scans via Anatomy-Aware Multi-Class Segmentation and Topology-Guided Iterative Learning.

Intrathoracic airway segmentation in computed tomography is a prerequisite for various respiratory d...

Multi-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition.

Gait recognition is a behavioral biometric technique that identifies individuals based on their uniq...

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